Image processing using hybrid lookup table

ABSTRACT

An image processing method and an image processing apparatus converts a binary image into a grayscale image by increasing the size and information quantity of the binary image. The image processing method includes generating a plurality of lookup tables having multiple gray level values assigned to a predetermined pattern of the binary image such that the gray level is retrieved from the plurality of lookup tables depending on the context of the block being processed. The binary image may be converted into the grayscale image by applying the gray value of one of the plurality of lookup tables that corresponds to the context, such as the frequency components of the binary image. With this configuration, since the size and information quantity of an image can be reduced for transmission, it is possible to prevent deterioration of image quality when the image is restored, while also printing at a higher speed.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims benefit of priority under 35 U.S.C. 119(a) fromKorean Patent Application No. 2006-0114119, filed on Nov. 17, 2006 inthe Korean Intellectual Property Office, the disclosure of which isincorporated herein by reference in its entirety.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present general inventive concept most closely relates to imageprocessing, and more particularly, to an image processing method and animage processing apparatus, which convert a binary image having lowresolution into a grayscale image having higher resolution.

2. Description of the Related Art

In general, performance of an image forming apparatus, such as aprinter, a multifunction copier, and so on, is determined by suchfactors as print speed and image quality. Factors affecting the printspeed include print resolution, print data transmission time from a hostapparatus, such as a computer system, to the image forming apparatus,print data processing time in either or both of the host apparatus andthe image forming apparatus, and printing time of a printer engine inthe image forming apparatus. Historically, printing speed has beendominated by certain mechanical limitations of the printer engine. Inrecent years, however, with improvements to increase the speed of theprinter engine, the print data transmission time and the print dataprocessing time have dominated the overall time to print.

The print data transmission time and the print data processing time aredetermined by a data exchange system between the host apparatus and theimage forming apparatus. For example, if a printer driver executing inthe host apparatus employs a graphics device interface (GDI) system thatperforms color matching, image rendering, etc., print data may becompressed by a compression algorithm in the host apparatus, such asJoint Bi-level Image Expert Group (JBIG), to reduce transmission timefor the data to traverse the distance from the host apparatus to theimage forming apparatus. The transmitted print data are thendecompressed and printed in the image forming apparatus. On the otherhand, if the printer driver employs a page description language (PDL)system, the print data are transmitted as, for example, descriptors thatare processed entirely in the image forming apparatus to render theimage, unlike the GDI system.

FIG. 1 is a flow chart illustrating an example of a conventional processof transmitting print data from a host apparatus to an image formingapparatus. Referring to FIG. 1, at operation S1 halftoning is performedin the host apparatus on an 8-bit grayscale image having a resolution of200×200 pixels and the grayscale image is converted into a one-bitbinary image having resolution of 200×200 pixels. Next, JBIG compressionis performed on the resultant binary image at operation S2 and thecompressed binary image is transmitted from the host apparatus to animage forming apparatus at operation S3. At operation S4, JBIGdecompression is performed on the binary image transmitted to the imageforming apparatus. In this case, a high-capacity page memory is requiredto store the 200×200 one-bit image. Additionally, a large quantity ofdata must be transmitted since the compressed binary image also has alarge quantity of data. If a page memory has the capacity to store animage transmitted from the host apparatus to the image formingapparatus, that is, there is a large quantity of transmission data, along time is required to transmit the data to fill the page memory,prior to which no printing is performed.

FIGS. 2A and 2B are flow charts illustrating other examples ofconventional processes to print data from a host apparatus to an imageforming apparatus. FIG. 2A illustrates a case where the amount of dataof an input image (hereinafter also referred to as “informationquantity”) is reduced. In this example, operations S11 to S14 aresimilar to the operations S1 to S4 in the example of FIG. 1. The exampleof FIG. 2A additionally includes operation S15, where the 200×200one-bit binary image decompressed in operation S14 is converted into a200×200 8-bit grayscale image. Consequently, operation S15 increases theinformation quantity.

FIG. 2B illustrates a case where the size of an input image is reduced.In the example of FIG. 2B, operations S21 to S24 are similar tooperations S11 to S14 in the example of FIG. 2A. The example of FIG. 2Badditionally includes operation S20 where the size of an input image isreduced from 200×200 pixels to 100×100 pixels. The process of FIG. 2Bfurther includes operation S25 to increase the spatial resolution of the100×100 one-bit binary image, decompressed in operation S24, to producea 200×200 one-bit binary image. The operation S25 expands the size ofthe image using an interpolation method or the like.

Using the above-described processes to reduce the information quantityor the size of the image prior to the transmission of the print data mayresult in a shortened transmission time. Specifically, print data tofill a page memory reduced to ⅛ its size otherwise can be transmitted inthe example of FIG. 2A and print data to fill a page memory reduced to ¼its size otherwise can be transmitted in the example of FIG. 2B.Subsequent to print data transmission, the data to render the image in adesired size and resolution can be obtained by increasing theinformation quantity or the number of pixels in the recovered image.

FIG. 3 is a flow chart illustrating a conventional resolution increasingmethod using a lookup table. As illustrated in FIG. 3, at operation S31a binary image is input, and an image process at operation S32 isperformed by accessing a lookup table (not illustrated) to retrieveimage data corresponding to an input block of the input binary image.When the image process is performed on all input blocks constituting thebinary image at operation S33, the resolution increasing method isterminated.

In such a conventional method, the image process is performed using onepredetermined lookup table irrespective of characteristics of the inputbinary image. In the case where the lookup table contains mean values ofpixels obtained from a representative training image, artifacts, such asblurring, may occur in a resultant image pattern after the imageprocessing of a specific input image.

SUMMARY OF THE INVENTION

The present general inventive concept provides an image processingmethod and an image processing apparatus to increase the resolution ofan image using a lookup table containing values for different imagecharacteristics.

Additionally, the present general inventive concept provides an imageprocessing method and an image processing apparatus to preventprocessing artifacts, such as blurring or a mosaic pattern, by applyinga value from the lookup table corresponding to characteristics of animage.

Additional aspects and utilities of the present general inventiveconcept will be set forth in part in the description which follows and,in part, will be obvious from the description, or may be learned bypractice of the present general inventive concept.

The foregoing and/or other aspects and utilities of the present generalinventive concept are achieved by providing an image processing methodof converting a binary image into a grayscale image comprisinggenerating a plurality of lookup tables having gray level valuesassigned to a predetermined pattern of the binary image such that theplurality of lookup tables corresponds to frequency components of thebinary image, and converting the binary image into the grayscale imageby retrieving the gray level value from one of the plurality of lookuptables that corresponds to one of the frequency components of the binaryimage.

The plurality of lookup tables may comprise an image lookup tablecorresponding to an image having a low frequency component and a textlookup table corresponding to an image having a high frequencycomponent, and the converting the binary image into the grayscale imagemay comprise retrieving the gray level value from one of the imagelookup table or the text lookup table according to a text ratio of thebinary image.

At least one of the image lookup table and the text lookup table may bedetermined using a pattern existing in the binary image and a mean valueof a plurality of gray level values corresponding to the pattern in thegrayscale image.

The text ratio may be a ratio of a frequency at which a pattern to beprocessed occurs in an overall training image to a frequency at whichthe pattern to be processed occurs in a training image having the highfrequency component.

The plurality of lookup tables may further comprise an overall lookuptable corresponding to the overall training image, and the convertingthe binary image into the grayscale image may comprise retrieving thegray level value from the image lookup table or the text lookup table ifa text ratio of the pattern to be processed falls within a first rangeand a difference between values of the image lookup table and the textlookup table is larger than a first threshold value.

The plurality of lookup tables may further comprise an overall lookuptable corresponding to the overall training image, and the convertingthe binary image into the grayscale image may comprise retrieving thegray level value from the image lookup table or the text lookup table ifa text ratio of the pattern to be processed is out of a first range andfrequency at which the pattern to be processed occurs in the overalltraining image falls within a second range.

The converting the binary image into the grayscale image may compriseretrieving the gray level value from the text lookup table if the numberof blocks adjacent to a block having the pattern to be processed andhaving the text ratio larger than a second threshold value is largerthan a third threshold value.

The generating the plurality of lookup tables may comprise determiningan optimized template using a genetic algorithm.

The foregoing and/or other aspects and utilities of the present generalinventive concept are also achieved by providing an image processingapparatus to convert a binary image into a grayscale image, comprising astoring part that stores a plurality of lookup tables having gray levelvalues assigned to a predetermined pattern of the binary image such thatthe plurality of lookup tables corresponds to frequency components ofthe binary image, and a converting part that converts the binary imageinto the gray image by retrieving the gray level value from one of theplurality of lookup tables that corresponds to one of the frequencycomponents of the binary image.

The foregoing and/or other aspects and utilities of the present generalinventive concept are also achieved by providing a method to convert abinary image into a multi-bit pixel image, comprising storing in amemory table a first multi-bit pixel value and a second multi-bit pixelvalue each addressed by a like bit pattern formed from states of binaryimage pixels, and assigning to a pixel in the multi-bit pixel image oneof the first multi-bit pixel value and the second multi-bit pixel valueaddressed by the like bit pattern corresponding to the states of thebinary image pixels in an image block of the binary image containing abinary pixel corresponding to the pixel in the multi-bit pixel image,the first multi-bit pixel value being assigned to the pixel when theimage block is in a first image context and the second multi-bit pixelvalue being assigned to the pixel when the image block is in a secondimage context.

The foregoing and/or other aspects and utilities of the present generalinventive concept are also achieved by providing a method to convert abinary image obtained from an image having a first resolution to amulti-bit pixel image having a second resolution, comprising forming ahybrid lookup table having a plurality of storage locations respectivelyaddressable by a plurality of bit patterns, at least two of the storagelocations being addressed by a like one of the bit patterns, storing ineach of the storage locations of the hybrid lookup table a multi-bitpixel value obtained from a multi-bit training image at a pixel locationcorresponding to a binary pixel in an image block of a binary trainingimage, the multi-bit training image having the second resolution, thebit pattern to address each of the storage locations being formed fromstates of a plurality of binary pixels in the image block of the binarytraining image, the at least two storage locations storing a firstmulti-bit pixel value and a second multi-bit pixel value, and assigningto a pixel in the multi-bit pixel image the first multi-bit pixel valuewhen the image block in the binary image having pixel statescorresponding to the like bit pattern is in a first image context andassigning the second multi-bit pixel value when the image block in thebinary image having the pixel states corresponding to the like bitpattern is in a second image context.

The foregoing and/or other aspects and utilities of the present generalinventive concept are also achieved by providing an apparatus to converta binary image into a multi-bit pixel image, comprising a storing partto store a first multi-bit pixel value and a second multi-bit pixelvalue each addressed by a like bit pattern formed from states of binaryimage pixels, and a converting part to assign to a pixel in themulti-bit pixel image one of the first multi-bit pixel value and thesecond multi-bit pixel value addressed by the like bit patterncorresponding to the states of the binary image pixels in an image blockof the binary image containing a binary pixel corresponding to the pixelin the multi-bit pixel image, the converting part to assign the firstmulti-bit pixel value to the pixel when the image block is in a firstimage context and to assign the second multi-bit pixel value to thepixel when the image block is in a second image context.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and/or other aspects and utilities of the present generalinventive concept will become apparent and more readily appreciated fromthe following description of the exemplary embodiments, taken inconjunction with the accompanying drawings, in which:

FIG. 1 is a flow chart illustrating an example of a conventional processof transmitting print data from a host apparatus to an image formingapparatus;

FIG. 2A is a flow chart illustrating another example of a conventionalprocess of transmitting print data from a host apparatus to an imageforming apparatus;

FIG. 2B is a flow chart illustrating yet another example of aconventional process of transmitting print data from a host apparatus toan image forming apparatus;

FIG. 3 is a flow chart illustrating a conventional resolution increasingmethod using a lookup table;

FIG. 4 is a flow chart illustrating a process of generating a hybridlookup table according to an exemplary embodiment of the generalinventive concept;

FIG. 5 is a view illustrating a process of calculating a cost associatedwith the calculation of a fitness value;

FIG. 6 is a flow chart illustrating a process of calculating optimizedweights using a genetic algorithm;

FIG. 7 is a view illustrating an initial string;

FIGS. 8A and 8B are views illustrating a process of crossover andmutation, respectively, of a genetic algorithm;

FIG. 9 is a view illustrating positions of pixels constituting atemplate optimized by a genetic algorithm;

FIG. 10 is a flow chart illustrating a process of expanding a 600 dpigrayscale image into a 1200 dpi grayscale image;

FIGS. 11A and 11B are views illustrating an existent pair existing in atraining image;

FIG. 12 is a flow chart illustrating a test process using a hybridlookup table;

FIGS. 13A and 13B are flow charts illustrating a process of generating aflag used as a condition in a test process;

FIG. 14 is a flow chart illustrating a process of applying a hybridlookup table to a block of a test image;

FIG. 15 is a view illustrating eight adjacent blocks PN around a block Pto be processed;

FIG. 16A is a view illustrating an example of a result of application ofone lookup table to a test image in the related art, and FIG. 16B is aview illustrating an example of a result obtained according to anexemplary embodiment of the present general inventive concept; and

FIG. 17 is a block diagram illustrating a configuration of an imageforming apparatus according to an exemplary embodiment of the presentgeneral inventive concept.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Reference will now be made in detail to the embodiments of the presentgeneral inventive concept, examples of which are illustrated in theaccompanying drawings, wherein like reference numerals refer to likeelements throughout. The exemplary embodiments are described below so asto explain the present general inventive concept by referring to thefigures.

In an image processing method according to an exemplary embodiment ofthe present general inventive concept, a binary image is converted intoa multi-bit pixel image by increasing the size and information quantityof the binary image. The exemplary image processing method of thisembodiment comprises operations to generate a memory table encompassinga plurality of lookup tables containing multi-bit pixel values addressedby bit patterns corresponding to predetermined pixel patterns of thebinary image. Such a memory table is hereinafter referred to as a“hybrid lookup table,” or hybrid LUT, and may contain multiple multi-bitpixel values for a given bit pattern so that a particular multi-bitimage pixel value, such as a gray level value, can be assigned to apixel in accordance with an “image context” of the pixel. As usedherein, an “image context” is a region in the image that can becharacterized by one or more defining features. An image context may bea block of pixels having features defined by, for example, the spatialfrequency content thereof, although other characteristics such asbrightness, pixilated boundaries, color gradients, etc., may be used todefine image contexts without departing from the spirit and intendedscope of the present general inventive concept. The hybrid lookup tablemay be used to convert the binary image into, for example, a grayscaleimage by retrieving associated gray level values from the one of theplurality of lookup tables that corresponds to the image context of eachpixel processed, such as, for example, the frequency content of theregion of the binary image in the neighborhood of the pixel. In theexemplary embodiments described below, the multi-bit pixel values aregray level values of a grayscale image, although the present generalinventive concept is not limited thereto.

FIG. 4 is a flow chart illustrating a process of generating the hybridlookup table according to an exemplary embodiment of the present generalinventive concept. As illustrated in FIG. 4, specifically, the operationof generating the hybrid lookup table in the illustrated exemplaryembodiment comprises operation S110 of determining an expansioncoefficient, operation S120 of determining a template, and operationS130 of performing training.

The expansion coefficient, as used herein, will refer to amultiplicative value corresponding to the increase in resolution fromthe input binary image to the final grayscale image. For example, if theexpansion coefficient is two (2), a 600×600 dpi binary input image willbe scaled to a resultant 1200×1200 dpi grayscale image. For purposes ofdemonstration only in the exemplary embodiment, the expansioncoefficient will be assigned a value of two (2) without being limitedthereto.

In operation S120, of FIG. 4, a search is conducted to find an“optimized” template, which, for a particular block size spanning ablock of pixels in a binary image, establishes which pixel positions areused to determine the grayscale value to be assigned to the pixel beingprocessed. As with many optimizing search procedures, a measure offitness allows solutions to be graded and, in certain procedures such asthe genetic algorithm described below, allows “better” solutions to beused as a base, or “parent,” from which the search is continued.

FIG. 5 is a view illustrating a configuration of pixels to demonstrate acalculation of an exemplary cost-based fitness value. As is illustratedin FIG. 5, an input grayscale image 51 and a restored image 52 aredivided into block units of a certain block size of pixels. Acorrelation coefficient ρ_(xy) may be calculated from a pair ofcorresponding blocks X and Y of the known input grayscale image 51 andthe restored image 52, respectively, according to the following equation1.

$\begin{matrix}{\rho_{xy} = \frac{\sigma_{xy}}{\sigma_{x}\sigma_{y}}} & \lbrack {{Equation}\mspace{20mu} 1} \rbrack\end{matrix}$

In equation 1, σ_(xy) is a covariance of X and Y, and σ_(x) and σ_(y)represent standard deviations of X and Y, respectively.

Next, correlation coefficients are calculated from all blocks, and thena cost, which may be an average of the correlation coefficients, iscalculated, such as by the following equation 2.

$\begin{matrix}{{Cost} = {\frac{1}{L}{\sum\limits_{i = 0}^{L - 1}\rho_{xy}}}} & \lbrack {{Equation}\mspace{20mu} 2} \rbrack\end{matrix}$

In equation 2, L is the number of blocks in the image. The cost functionof equation 2 evaluates how close a restored image is to the originalimage, and therefore is suitable as a fitness measure. It is to beunderstood, however, that other fitness measures may be used inconjunction with the present general inventive concept without departingfrom the spirit and intended scope thereof.

Next, operation S120 of determining a template will be described. In theoperation S120 of determining a template, an optimized template may bedetermined using an evolutionary search process, such as a geneticalgorithm, that obtains positions of pixels constituting the template.It is to be understood, however, that other procedures for determiningtemplates may be used in conjunction with the present general inventiveconcept without deviating from the spirit and intended scope thereof.The template may be used to evaluate respective states of the binarypixels that are members of the template in the neighborhood of the pixelbeing processed, where the states of the member pixels define the bitpattern to address into the hybrid LUT. Ultimately, the pixel beingprocessed is to visually appear to be nearly, or, ideally, exactly thesame gray level as the image that was halftoned to produce the binaryimage on which the template is applied.

FIG. 6 is a flow chart illustrating an exemplary process of calculatingoptimized weighting by member pixels of the template using a geneticalgorithm. The exemplary genetic algorithm of FIG. 6 may be realized asprocessor instructions executed on a processor to search for anoptimized template. In certain embodiments of the present generalinventive concept, the genetic algorithm is executed on processingequipment separate from the image forming apparatus.

Referring to FIG. 6, an initial population of strings is determined atoperation S201. The initial population may be a randomly selected set ofstrings having the bits thereof representing whether a correspondinglocation in the template is a member pixel thereof.

FIG. 7 is a view illustrating an exemplary string. The exemplary stringforms a weighting scheme in that it indicates the member pixels of thetemplate that will be used to set the gray level of a pixel beingprocessed. As illustrated in FIG. 7, the exemplary string 73 has a valueof “0” corresponding to a pixel position in an image processing block 70that is not a member of the template and a value of “1” corresponding toa pixel position in the image processing block 70 that is a member ofthe template. In FIG. 7, the exemplary template is configured to processa center pixel 71 of the image processing block 70 indicated by “a” inaccordance with the binary state of the pixels at positions 72 in theblock 70 indicated by “b.” It is to be understood that other templateconfigurations may be used in conjunction with the present generalinventive concept without departing from the spirit and intended scopethereof. For example, the template may be optimized to have the pixel tobe processed, i.e., the pixel at position “a,” in a position other thanat the center position of the image process block.

Referring once again to FIG. 6, once the initial strings have beenselected, the process transitions to operation S202, in which a hybridLUT is determined. The hybrid LUT, ultimately, is populated to associatea gray level value with one or more corresponding patterns of bit statesof member binary pixels of the template. Additionally, as discussedabove, the gray level value that is assigned may be a function of theimage context of the pixel being processed. For example, in certainembodiments of the present general inventive concept, the gray levelvalue is set in accordance with an image context defined by spatialfrequency components of the image in the neighborhood of the pixel beingprocessed. Thus, in these embodiments, the hybrid LUT may contain morethan one gray value for an encountered pattern. This may also beachieved, as will be described below, by a separate LUT for each imagecontext, such as for each of a predetermined number of spatial frequencybands.

Once the template has been optimized, the LUT may contain the results ofthe training, which is described below. Thus, the template optimizationmay be achieved in conjunction with, or concurrently with the trainingoperation, or, alternatively, the hybrid LUT may be populated duringtemplate optimization with images other than the training images.Alternatively still, known gray values visually associated with halftonepatterns may populate the LUT. However, during the templateoptimization, gray level values for the chosen image characteristic,such as individual spatial frequency bands, should be included in theLUT so that the template can be evaluated for fitness.

Once the hybrid LUT has been created, the template is applied to animage at operation S203 using the lookup table determined in theoperation S202.

Returning to FIG. 6, a fitness value is calculated for each string inthe population at operation S204, such as through the cost function ofequation 2. In certain embodiments of the present general inventiveconcept, the strings may be ordered as to their fitness and it may thenbe determined at operation S205 whether the most fit string meets theoptimal fitness criteria, such as the cost function of equation 2exceeding a threshold value. If it has been ascertained that a stringmeets the optimal fitness criteria, the hybrid LUT determining processof FIG. 6 may terminate. If, however, no string is found to be optimalat operation S205, the breeding procedures of the genetic algorithm areexecuted at operations S206 to S208. The breeding procedures in thepresent exemplary embodiment include reproduction (operation S206),crossover (operation S207) and mutation (operation S208).

First, the reproduction operation (S206) is performed to establish whichstrings will serve as parents for the next generation of solutions. Theparent strings are paired according to a reproduction probabilitydetermined from the fitness value calculated for each string using thefollowing equation 3.

$\begin{matrix}{{P(i)} = \frac{F(i)}{\sum\limits_{j = 1}^{n}{F(j)}}} & \lbrack {{Equation}\mspace{20mu} 3} \rbrack\end{matrix}$

In equation 3, P(i) and F(i) represent a reproduction probability and afitness value of an i-th string, respectively, and n is the total numberof strings in the population. In certain embodiments of the presentgeneral inventive concept, each string will be paired with a number ofother strings proportional to its reproduction probability for purposesof breeding a pair of children strings. In this manner, the most fitstrings are used the most number of times in producing the nextgeneration of strings.

FIGS. 8A and 8B are views illustrating the crossover operation (S207)and the mutation operation (S208), respectively. First, referring toFIG. 8A, two strings 81 and 82 are selected for the crossover operation(S207). A predetermined number of bits of the two strings 81 and 82 areinterchanged around a predetermined crossover position (see referencenumerals 84 and 85 in FIG. 8A) to create a pair of children strings thatare added to the population of strings.

Subsequently, referring to FIG. 8B, a predetermined number of strings,such as that representatively illustrated as string 86, are selected forthe mutation operation (S208) and a position of the mutation operation(S208) is selected. The mutation operation (S208) inverts a binary valueof the bit in the string 86 corresponding to the position of themutation operation (see reference numeral 88 in FIG. 8B). The mutationoperation (S208) prevents the genetic algorithm from converging on localextrema to thereby fail in finding a global solution matching thefitness criteria.

At operation S209 of FIG. 6, the string population is evaluated todetermine which solution shall serve as the template in the nextiteration of the process. Since a string's fitness as a template isdetermined after the template has been applied to an image, certainembodiments of the present general inventive concept select a childstring from the previously most fit string as the template. More thanone string from a generation may be tested for its fitness as a templateprior to breeding a subsequent generation. Additionally, in operationS209, strings that have a reproduction probability less than apredetermined threshold may be removed from the population so as tominimize breeding operations that would be unlikely to produce childrenstrings closer to the optimum. Once the new population has beenestablished, the process of FIG. 6 is reiterated at the hybrid LUTforming operation S202.

FIG. 9 is a view illustrating positions of pixels constituting anexemplary template optimized by the genetic algorithm. In FIG. 9, it isassumed that the image processing block size of the template 90 is 5×5and the number of member pixels used in the template 90 is 16. In FIG.9, pixels 91, 92 and 93 indicated by “a”, “b” and “c,” respectively,constitute the optimized template 90. In this case, the pixel 91indicated by “a” is a pixel to be processed. The pixels 92 indicated by“b” may be located by way of the optimization to have more effect on theresult of the hybrid resolution improving process of the present generalinventive concept, to be discussed below, while the pixels 93 indicatedby “c” may be located to have less effect on the result of the hybridresolution improving process. For example, when a principaldistance-based error diffusion process is used to convert the originalgrayscale image into the binary image, the template may be constructedsuch that pixels at positions from which the majority of errors werediffused into the pixel to be processed are included in the template atpositions therein having a greater influence on the resulting restoredimage.

Having obtained the optimized template, the embodiment of the presentgeneral inventive concept referred to hereinafter as the “hybridresolution improving process” is trained as indicated in FIG. 4 atoperation S130. Briefly, the training operation uses a halftoned imagefor which corresponding gray levels are known and populates the hybridLUT with the associated gray levels for each pattern encountered in thetraining image. Thus, as indicated above, the template optimization maybe performed concurrently with the training, where each candidatetemplate is tested against the training image or images, and when theoptimized template has been found, the hybrid LUT would then already bepopulated and trained.

As illustrated in FIG. 4, the operation S130 of performing the trainingcomprises sub operation S131 of determining a training image, suboperation S132 of populating the hybrid LUT with the existent pairs ofpatterns and gray level values that exist in the training image. FIG. 4also depicts sub operation S133 of estimating a non-existent pair for apattern that does not exist in the training image.

In the sub operation S131 of determining the training image, thetraining image is selected. The selection of the training image has aneffect on the result of the hybrid resolution improving process in thatthe image characteristics of the training image will be carried into theimages processed subsequent to such training. For example, when a hybridlookup table trained on a training image of spatially arranged pixelclusters to have significant energy in high frequency bands is appliedto an image having similar frequency characteristics, the resultantimage quality is improved over results obtained otherwise. Similarly,when a hybrid lookup table trained on a training image of pixelsdistributed to contain primarily low frequency components is applied toan image of primarily low frequency content, a higher image quality isobtained over other cases. Accordingly, certain embodiments of thepresent general inventive concept implement a hybrid lookup tablethrough an optimized training image, as will be presently described.

According to certain embodiments of the present general inventiveconcept, the training image is an assemblage of images, each imagehaving image blocks in various image contexts to be used in training.Additionally, the training image may have the expansion factor appliedthereto such that a binary input image is expanded to a higherresolution grayscale image. For example, the training image may beformed as a pair of images each selected for its content ofcorresponding frequency components. In this exemplary embodiment, wherethe value of the expansion factor is assumed to be two (2), the trainingimage may be constituted by a 600 dpi binary image and a 600×2=1200 dpigrayscale image, which is the lower resolution multiplied by theexpansion factor. Here, the 600 dpi binary image may be generated byperforming halftoning on a 600 dpi grayscale image. A halftoning methodused in this exemplary embodiment may be a principal distance-basederror diffusion method using visual estimation.

In certain embodiments of the present general inventive concept, the1200 dpi grayscale image is an interpolation of the 600 dpi grayscaleimage. In this exemplary embodiment, the interpolation method is chosendepending on the characteristics of the image context. FIG. 10 is a flowchart illustrating an exemplary process of expanding the 600 dpigrayscale image into the 1200 dpi grayscale image. Referring to FIG. 10,it is first determined at operation S1311 whether the 600 dpi grayscaleimage from which the 600 dpi binary image is obtained contains text,which is characterized by high energy content in high frequency bands.If it is determined at operation S1311 that the 600 dpi grayscale imagecontains text, a zero order interpolation may be applied to expand the600 dpi grayscale image into the 1200 dpi grayscale image at operationS1312.

On the other hand, if the 600 dpi grayscale image is composed ofsmoothly distributed features so as to contain a majority of lowfrequency components, such as where the 600 dpi grayscale image does notcontain text, a bicubic interpolation may be applied to expand the 600dpi grayscale image into the 1200 dpi grayscale image at operationS1313. In certain embodiments of the present general inventive concept,the training image is formed of a first 600 dpi binary image halftonedfrom a first 600 dpi gray scale image containing a large percentage oftext and a corresponding first 1200 dpi image interpolated from thefirst 600 dpi gray scale image through a zero order interpolationprocess, a second 600 dpi binary image halftoned from a second 600 dpigrayscale image containing primarily smooth features, and acorresponding second 1200 dpi image interpolated from the second 600 dpigrayscale image through a bicubic interpolation process. Alternatively,the training image may be a 600 dpi binary image halftoned from an imagecontaining both high and low frequency components and a corresponding1200 dpi grayscale image constructed to sufficiently maintain thefrequency composition of the original grayscale image. The presentgeneral inventive concept may be implemented with a variety of trainingimages without departing from the spirit and intended scope thereof.Moreover, many training schemes that can be used in conjunction with thepresent general inventive concept will become apparent to the skilledartisan upon review of this disclosure. The scope of the present generalinventive concept is intended to encompass all such alternative trainingschemes.

Next, the sub operation S132 of the training process S130 will bedescribed. In the sub operation S132, the hybrid lookup table ispopulated with “existent pairs” of the training image. As used herein,an “existent pair” is a bit pattern in the binary image and itscorresponding gray level in the grayscale image. A bit pattern maycorrespond to one gray level value at one location in the training imageand may correspond to another gray level value elsewhere in the trainingimage. Certain embodiments of the present general inventive conceptinclude consideration of these instances, as will be detailed inparagraphs that follow.

FIGS. 11A and 11B are views illustrating existent pairs of the trainingimage. FIG. 11A depicts one existent pair, where it is assumed that, forpurposes of simple illustration and not limitation, a template 110 has ablock size of 3×3 pixels. In FIG. 11A, a reference numeral 111 denotes aposition in the template 110 of a pixel to be processed. If a pattern112 of member pixels of the template 110 exists in the 600 dpi binaryimage, there exists a corresponding gray level value 113 in the 1200 dpigrayscale image at the location of the pattern 112. The gray level value113 in the 1200 dpi grayscale image and the corresponding pattern in the600 dpi binary image having the pattern 112 comprise a single existentpair.

FIG. 11B depicts the case where two or more existent pairs for the samepattern of member pixels of the template 210 exists. In FIG. 11B, areference numeral 211 denotes a position in the image processing blockof a pixel to be processed, and a reference numeral 212 denotes thepattern 212 of member pixels of the template. If there are multiple graylevel values assigned to a given pattern in the hybrid LUT, then aplurality of existent pairs exists. Only one gray level value from amongthese existent pairs can be assigned to a multi-bit pixel, so certainembodiments of the present general inventive concept assign a mean valueof the gray level values to the existent pair.

However, to prevent artifacts such as blurring from occurring in, forexample, an edge region of a restored image, as a consequence of using amean gray level for all occurrences of a pattern, certain embodiments ofthe present general inventive concept may populate multiple lookuptables, each containing a grayscale value corresponding to a differentimage context. For example, embodiments of the present general inventiveconcept provide a hybrid LUT containing a separate gray level value foreach band of frequency components in the training image. For purposes ofdemonstration and not limitation, the hybrid lookup table to bedescribed in the exemplary embodiment that follows comprises an imagelookup table corresponding to low frequency image contexts and a textlookup table corresponding to high frequency image contexts. Otherembodiments may include an overall lookup table optimized on a genericoverall training image that may include a selected distribution offrequencies to define other image contexts on which to train.

Referring to FIG. 11B, the plurality of exemplary existent pairs may bedivided into existent pairs 213 corresponding to low frequencycomponents in the training image and existent pairs 214 corresponding tohigh frequency components in the training image.

The image lookup table stores a mean value 215 of gray level values ofthe existent pairs 213 for a given pattern existing in the imagecontaining the low frequency components, while the text lookup tablestores a mean value 216 of gray level values of the existent pairs 214for a given pattern existing in the image containing the high frequencycomponents, even when the patterns are equivalent in both LUTs. Each LUTis accessed during normal processing, i.e., after training has beencompleted, by a binary number formed from states in the binary imagepixels that are member pixels of the template and returns a gray levelvalue, such as the mean gray level value of all training occurrences ofthe pattern in the corresponding training image, from the LUTcorresponding to the image context, which in the case of theillustrative embodiment, is the spatial frequency content of the image.

In the examples described above, the plurality of existent pairs areclassified by the associated low and high frequency content and thepixel patterns of the existent pairs are used to access the LUTcorresponding to the sought frequency component, and the gray level ofthe existent pair, stored as, for example, the mean value of the graylevels, is retrieved from the image lookup table or the text lookuptable.

Next, the sub operation S133 of estimating the non-existent pair will bedescribed. In the sub operation S133 of estimating the non-existentpair, a best linear estimation, such as that disclosed in “Look-Up Table(LUT) Method for Inverse Halftoning,” Murat Mese and P. P. Vaidyanathan,IEEE Transactions on image processing, Vol. 10, No. 10, Oct. 2001, maybe used to estimate gray level values of existent pairs that do notexist in the training image.

A non-existent pair refers to a pattern of input pixels and that is notencountered in the training image during training and, as such, does nothave a gray value assigned thereto. If the number of non-existent pairsin the image being processed is between 10% and 30% of the total numberof image processing blocks in the image, estimated output values of thenon-existent pairs are satisfactory with regard to the overall qualityof the resultant image.

The following equation 4 expresses a relationship between an existentpair and a non-existent pair.

$\begin{matrix}{{\underset{A}{\begin{bmatrix}p_{0,0} & p_{0,1} & \cdots & p_{0,{N - 1}} \\p_{1,0} & p_{1,1} & \cdots & p_{1,{N - 1}} \\\vdots & \vdots & \vdots & \vdots \\p_{{M - 1},0} & p_{{M - 1},1} & \cdots & p_{{M - 1},{N - 1}}\end{bmatrix}}\underset{x}{\begin{bmatrix}x_{0} \\x_{1} \\\vdots \\x_{N - 1}\end{bmatrix}}} = \underset{b}{\begin{bmatrix}{b(0)} \\{b(1)} \\\vdots \\{b( {m - 1} )}\end{bmatrix}}} & \lbrack {{Equation}\mspace{20mu} 4} \rbrack\end{matrix}$

In equation 4, M is the number of existent pairs and N is the number ofmember pixels constituting a template. The rows of the matrix A arerespective bit patterns of existent pairs, and the elements of vector bare the respective gray level values corresponding to the existent pair.For example, a first row [p_(0,0) p_(0,1) . . . p_(0,N-1)] of the matrixA represents a first existent pattern, and b(0) represents a gray levelvalue corresponding to the first existent pattern.

Next, when a weight vector x is determined according to the followingequation 5, gray level values LUT(P) corresponding to non-existentpatterns are calculated according to the following equation 6.

$\begin{matrix}{x = {( {A^{T}A} )^{- 1}A^{T}b}} & \lbrack {{Equation}\mspace{20mu} 5} \rbrack \\{{{LUT}(P)} = \{ \begin{matrix}{0,} & {{{if}\mspace{14mu} y} < 0} \\{255,} & {{{if}\mspace{14mu} y} > 255} \\{{{round}(y)},} & {otherwise}\end{matrix} } & \lbrack {{Equation}\mspace{20mu} 6} \rbrack\end{matrix}$

In equation 6, P represents a non-existent pattern, and y represents anestimated gray level value corresponding to the non-existent pattern. Incertain embodiments of the present general inventive concept, theestimated gray level value LUT(P) is stored in the LUT under the patternx for use in processing.

FIG. 12 is a flow chart illustrating an exemplary image processingprocedure using a generated hybrid lookup table in accordance withcertain embodiments of the present general inventive concept. Asillustrated in FIG. 12, a 600 dpi grayscale image is input for, say,resolution doubling, and a 600 dpi binary image is generated using ahalftoning method at operation S301. The halftoning method used in thepresent exemplary embodiment may be the same as the halftoning methodused to generate the binary image in the training process. Taking theexpansion coefficient to be two (2) then, at operation S302, the 600 dpibinary image generated in the operation S301 is converted into a 1200dpi grayscale image through data stored in the hybrid lookup tablegenerated as above.

Certain embodiments of the present general inventive concept includemechanisms to determine the image context of an input block. Forexample, when the image context is defined by frequency content of animage neighborhood, and the image context of data being evaluated isdetermined from the frequency band characterizing the region surroundingthe data being evaluated, then such mechanisms may indicate which graylevel value, e.g., the level from the low frequency lookup table or thelevel from the high frequency lookup table, should be retrieved. Theexemplary embodiment described below includes such a mechanism fordetermining which gray level value to retrieve, but it is to beunderstood that other such mechanisms may be used in conjunction withthe present general inventive concept without deviating from the spiritand intended scope thereof.

The hybrid lookup table of the following exemplary embodiment comprisesthree lookup tables: an image lookup table imageLUT trained on lowspatial frequency content, a text lookup table textLUT trained on highspatial frequency content, and an overall lookup table LUT trained onmixed spatial frequency content from an overall training image, asdescribed above.

FIGS. 13A and 13B are flow charts illustrating an exemplary process ofestablishing which lookup table to access to retrieve the gray levelvalue. In the exemplary test process illustrated in FIGS. 13A and 13B,one-bit data are stored in a first flag and a second flag correspondingto image blocks being scrutinized. The first flag maintains an indicatoras to which of the three generated hybrid lookup tables (the imagelookup table imageLUT, the text lookup table textLUT, and the overalllookup table LUT) is to be accessed to retrieve the gray level value.The second flag maintains an indicator as to which of the image lookuptable imageLUT and the text lookup table textLUT is to be accessed forthe gray level.

FIG. 13A is a flow chart illustrating a method of determining the stateof the first flag. In the present exemplary embodiment, it is to beassumed that the template is optimized to a 5×5 block, such as by thetemplate optimization processes described above, and lookup tables havebeen populated with patterns corresponding to the optimized templatehaving, for example, 16 member pixels, such as by the trainingprocedures described above. Then, as illustrated in FIG. 13A, an inputblock of 5×5 pixels of the overall training binary image is provided tooperation S401 and is analyzed to obtain its pixel pattern of memberpixels in the template. A “text ratio” of the 5×5 pattern of the blockis calculated at operation S402. As used herein, the “text ratio” refersto a ratio of the frequency at which a pattern to be processed occurs inthe overall training image to the frequency at which the pattern to beprocessed occurs in the training image containing the high frequencycomponents, e.g., the textLUT. The text ratio may be expressed by thefollowing equation 7.

$\begin{matrix}{{Textratio} = \frac{{frequency}_{total}\lbrack P\rbrack}{{frequency}_{text}\lbrack P\rbrack}} & \lbrack {{Equation}\mspace{20mu} 7} \rbrack\end{matrix}$

Here, P represents the pattern of template member pixels in the 5×5block to be processed, frequency_(total)[P] represents frequency atwhich the pattern P occurs in the overall training image,frequency_(text)[P] represents frequency at which the pattern P occursin the training image containing the high frequency components, such astext. The frequency of occurrence of the pattern P in an image is not tobe confused with spatial frequency, but rather is a rate of recurrenceof the pattern P in the image. Such rate may be expressed as a number ofoccurrences in the image or in respective blocks thereof.

Next, it is determined at operation S403 whether the calculated textratio of the block to be processed falls within a range of 0.3 to 0.7.If it is determined at operation S403 that the text ratio falls within arange of 0.3 to 0.7, a difference |diff| between the gray levels in theimage lookup table imageLUT and the text lookup table textLUTcorresponding to the pattern P is calculated at operation S404, and itis determined at operation S405 whether the difference |diff| is greaterthan a first threshold value. If it is determined at operation S405 thatthe difference |diff| is greater than the first threshold value, thefirst flag (first flag [P]) corresponding to the pattern P is set, suchas by activation into a “1” state, at operation S406. If it isdetermined at operation S405 that the difference |diff| is not greaterthan the first threshold value, the first flag (first flag [P]) isreset, such as by activation into a “0” state, at operation S407. Inaccordance with the present exemplary embodiment, the first thresholdvalue is set to be 0.3 obtained through visual estimation of a resultantimage according to the hybrid resolution improving method.

On the other hand, if it is determined at operation S403 that the textratio of the pattern P to be processed does not fall within a range of0.3 to 0.7, it is determined at operation S408 whether the frequency atwhich the pattern P to be processed occurs in the overall training imageis larger than 0 and less than a predetermined upper bound, for example,500. If it is determined at operation S408 that the frequency ofoccurrence in the overall training image of the pattern P is larger than0 and less than 500, the first flag (first flag [P]) is set at operationS406. On the other hand, if it is determined that such frequency ofoccurrence is greater than the upper bound, e.g., 500, the first flag(first flag [P]) is reset at operation S407.

Next, after the operation S406 or S407, it is determined whether a blockremains to be processed at operation S409. If it is determined atoperation S409 that a block remains to be processed, the processproceeds to operation S410 to repeat the operations S402 to S409.

FIG. 13B is a flow chart illustrating a process of determining the stateof the second flag. As illustrated in FIG. 13B, a 5×5 block of theoverall training binary image is provided to operation S501 and the textratio of the 5*5 pattern is calculated at operation S502. It is thendetermined at operation S503 whether the calculated text ratio isgreater than a second threshold value. If it is determined at operationS503 that the text ratio is greater than the second threshold value, thesecond flag (second flag [P]) of the pattern P to be processed is set atoperation S504, such as by activation into a “1” state. If it isdetermined at operation S503 that the text ratio is not greater than thesecond threshold value, the second flag (second flag [P]) is reset, suchas by activation into a “0” state, at operation S505. In this exemplaryembodiment, the second threshold value is set to be 0.6 as determinedthrough visual estimation of a resultant image according to the hybridresolution improving method.

After the operation S504 or S505, it is determined at operation S506whether there is a block remaining to be processed. If it is determinedat operation S506 that a block remains to be processed, the processproceeds to operation S507 to repeat the operations S502 to S506.

FIG. 14 is a flow chart illustrating an exemplary process through whicha hybrid lookup table is accessed to process a block of the image to beprocessed in accordance with embodiments of the present generalinventive concept. In this exemplary embodiment, the test image is a 600dpi binary image, the size of the image processing block is 5×5 pixels,and the number of member pixels in the template is 16.

As illustrated in FIG. 14, a 5×5 block of the 600 dpi binary image isprovided to operation S601 and is evaluated as to its pattern of memberpixels of the template. It is then determined at operation S602 whetherthe first flag (first flag [P]) corresponding to the input pattern P isset. If it is determined at operation S602 that the first flag (firstflag [P]) is activated into a “1” state, one of the image lookup tableimageLUT or the text lookup table textLUT is accessed at operations S603to S611. On the other hand, if it is determined at operation S602 thatthe first flag (first flag [P]) is reset, the gray level value for thepattern is obtained from the overall lookup table LUT at operation S612.

If it is determined at operation S602 that the first flag (first flag[P]) is set, the counters N and C are initialized to zero at operationS603, where N maintains a number of 5×5 blocks around the blockcontaining pattern P that have been evaluated for the state ofrespective second flags thereof, as will be described below and Cmaintains the number of adjacent 5×5 blocks PN having an activatedsecond flag (second flag [PN]). Here, C is a value between 1 and 8. FIG.15 is a view depicting eight adjacent blocks PN around a block P thatcontains the pattern to be processed.

Next, the adjacent blocks PN are respectively provided to operationS604, and each is evaluated at operation S605 as to whether the secondflag (second flag [PN]) thereof is set. If it is determined at operationS605 that the second flag (second flag [PN]) is set, C and N areincremented by one at operation S606. On the other hand, if it isdetermined at operation S605 that the second flag (second flag [PN]) ofthe adjacent block PN is reset, N is incremented by one and C remainsunchanged at operation S607.

Next, it is determined at operation S608 whether N has been incrementedto be equal to the number of adjacent blocks, which is eight (8) in theillustrated embodiment. If it is determined at operation S608 that Ndoes not equal 8, the operations S604 to S608 are repeated. That is, itis determined whether the second flags corresponding to all of the eightadjacent blocks PN around the block P to be processed are set.

If it is determined at operation S608 that N equals 8, it is thendetermined at operation S609 whether C is greater than a third thresholdvalue. If it is determined at operation S609 that C is larger than thethird threshold value, the gray level value corresponding to the patternP is obtained from the text lookup table textLUT at operation S610. Inother words, if the number of the eight adjacent blocks PN around theblock P to be processed having a text ratio greater than the secondthreshold value is greater than the third threshold value, the textlookup table textLUT is accessed to obtain the gray level valuecorresponding to the pattern to be processed. In certain embodiments ofthe present general inventive concept, the third threshold value is setto 0 as obtained through visual estimation of a resultant imageaccording to the hybrid resolution improving method.

On the other hand, if it is determined at operation S609 that C is lessthan the third threshold value, the gray level value for the pattern Pis retrieved from the image lookup table at operation S611.

After the operation S610, S611 or S612, it is determined at operationS613 whether a block remains to be processed at operation S613. If it isdetermined at operation S613 that a block remains to be processed, theprocess proceeds to operation S614 to repeat the operations S601 toS613. As described above, the lookup table from which the gray levelvalue is retrieved is determined from values of the first and secondflags, the states of which are indicative of the characteristics of theimage context.

FIG. 16A is a view illustrating an example of a result of application ofone lookup table to a test image in the related art, and FIG. 16B is aview illustrating an example of a result obtained according to anexemplary embodiment of the present general inventive concept. It can bereadily observed from FIGS. 16A and 16B that artifacts such as blurringare greatly decreased by way of application of the present generalinventive concept. Accordingly, increase of information quantity andexpansion of the size of print data after print data transmission can beachieved simultaneously, and when implemented in accordance with theexemplary embodiment described above, print data reduced by 1/32 can betransmitted and received into a lower capacity page memory.

FIG. 17 is a schematic block diagram illustrating a configuration of animage forming apparatus 100 according to an exemplary embodiment of thegeneral inventive concept. The image forming apparatus 100 may perform aprinting operation to a print medium such as a paper, based on printdata transmitted from a host apparatus 200. The image forming apparatus100 may be, for example, a printer, a multifunction copier, or the like.The host apparatus 200 may be, for example, a computer system.

As illustrated in FIG. 17, the image forming apparatus 100 comprises aninput part 110, a storing part 120, a converting part 130 and an imageforming part 140. The input part 110 communicates with the hostapparatus 200 and receives print data of a binary image from the hostapparatus 200. It is to be understood that the compartmentalization ofthe functionality illustrated through the components of FIG. 17 issuited to describe the features of the present general inventiveconcept, but other configurations may be used with the present generalinventive concept without departing from the spirit and intended scopethereof. For example, certain functionality of components illustrated inFIG. 17 as discrete can be combined into a single component, whereasothers may be distributed across multiple components. Moreover, any ofthe components may be implemented in hardware, software or a combinationof both. Other variants may become apparent to the skilled artisan uponreview of this disclosure, and the present general inventive concept isintended to embrace all such variants.

The storing part 120 stores a plurality of lookup tables such as thosedescribed above having gray level values corresponding to patterns ofthe binary image. The storing part 120 may further include processinginstructions to be executed by a processor so as to carry out one ormore features of the present general inventive concept.

The converting part 130 converts an input binary image into a grayscaleimage by retrieving a gray level value from one of the plurality oflookup tables stored in the storing part 120 that corresponds to theapplicable characteristic of the image context, such as the frequencycontent thereof. The converting part 130 may perform embodiments of theimage processing method of the present general inventive concept asexemplified above with reference to FIGS. 12 to 15 throughinterconnected circuit elements, through implementation of a processorexecuting processing instructions, or a combination of both.

The image forming part 140 performs a printing operation on a printmedium based on the grayscale image generated in the converting part130. The present general inventive concept is not limited by the systemconfiguration of the image forming part 140 and many such devices may beused in conjunction with the present invention, including ink-baseddevices and toner-based devices.

As apparent from the above description, the present general inventiveconcept provides an image processing method and an image processingapparatus, which are capable of increasing the resolution of an imageusing a plurality of lookup tables.

Specifically, a resultant image without artifacts such as blurring or amosaic pattern can be obtained by implementing a hybrid lookup tableincluding a separate gray level value for a context dependent pixelpattern, such as was demonstrated through the image lookup table, thetext lookup table, and the overall lookup table described above.

Accordingly, since the size and information quantity of an image can bereduced, it is possible to prevent deterioration of image quality whenthe image is restored, thereby performing a printing operation at ahigher speed.

Although a few exemplary embodiments of the present general inventiveconcept have been illustrated and described, it will be appreciated bythose skilled in the art that changes may be made in these exemplaryembodiments without departing from the principles and spirit of thegeneral inventive concept, the scope of which is defined in the appendedclaims and their equivalents.

1. An image processing method of converting a binary image into agrayscale image, comprising: generating a plurality of lookup tableshaving gray level values assigned to a predetermined pattern of thebinary image such that the plurality of lookup tables corresponds tofrequency components of the binary image, wherein the plurality oflookup tables includes an image lookup table corresponding to an imagehaving a low frequency component and a text lookup table correspondingto an image having a high frequency component; and converting the binaryimage into the grayscale image by retrieving a gray level value from oneof the plurality of lookup tables that corresponds to one of thefrequency components of the binary image, wherein the converting thebinary image into the grayscale image includes retrieving the gray levelvalue from one of the image lookup table or the text lookup tableaccording to a text ratio of the binary image, the text ratio being aratio of a frequency at which a pattern to be processed occurs in anoverall training image to a frequency at which the pattern to beprocessed occurs in a training image having the high frequencycomponent.
 2. The image processing method according to claim 1, whereinat least one of the image lookup table and the text lookup table isdetermined using a pattern existing in the binary image and a mean valueof a plurality of gray level values corresponding to the pattern in thegrayscale image.
 3. The image processing method according to claim 1,wherein the plurality of lookup tables further comprises an overalllookup table corresponding to the overall training image, and whereinthe converting the binary image into the grayscale image comprisesretrieving the gray level value from the image lookup table or the textlookup table if the text ratio of the pattern to be processed fallswithin a first range and a difference between values of the image lookuptable and the text lookup table is larger than a first threshold value.4. The image processing method according to claim 1, wherein theplurality of lookup tables further comprises an overall lookup tablecorresponding to the overall training image, and wherein the convertingthe binary image into the grayscale image comprises retrieving the graylevel value from the image lookup table or the text lookup table if thetext ratio of the pattern to be processed is out of a first range and afrequency at which the pattern to be processed occurs in the overalltraining image falls within a second range.
 5. The image processingmethod according to claim 4, wherein the converting the binary imageinto the grayscale image comprises retrieving the gray level value fromthe text lookup table if a number of blocks adjacent to a block havingthe pattern to be processed and having the text ratio larger than asecond threshold value is larger than a third threshold value.
 6. Theimage processing method according to claim 1, wherein the generating theplurality of lookup tables comprises determining an optimized templateusing a genetic algorithm.
 7. An image processing apparatus to convert abinary image into a grayscale image, comprising: a storing part thatstores a plurality of lookup tables having gray level values assigned toa predetermined pattern of the binary image such that the plurality oflookup tables corresponds to frequency components of the binary image,wherein the plurality of lookup tables includes an image lookup tablecorresponding to an image having a low frequency component and a textlookup table corresponding to an image having a high frequencycomponent; and a converting part that converts the binary image into thegrayscale image by retrieving a gray level value from one of theplurality of lookup tables that corresponds to one of the frequencycomponents of the binary image, wherein the converting the binary imageinto the grayscale image includes retrieving the gray level value fromone of the image lookup table or the text lookup table according to atext ratio of the binary image, the text ratio being a ratio of afrequency at which a pattern to be processed occurs in an overalltraining image to a frequency at which the pattern to be processedoccurs in a training image having the high frequency component.
 8. Amethod to convert a binary image into a multi-bit pixel image, themethod comprising: storing in a memory table a first multi-bit pixelvalue and a second multi-bit pixel value each addressed by a like bitpattern formed from states of binary image pixels; assigning to a pixelin the multi-bit pixel image one of the first multi-bit pixel value andthe second multi-bit pixel value addressed by the bit patterncorresponding to the states of the binary image pixels in an image blockof the binary image containing a binary pixel corresponding to the pixelin the multi-bit pixel image, the first multi-bit pixel value beingassigned to the pixel when the image block in the binary image is in afirst image context and the second multi-bit pixel value being assignedto the pixel when the image block in the binary image is in a secondimage context; and determining an optimum template of locations in theimage block from which to obtain the states of the binary image pixels,the determining of the optimum template including selecting the templatefrom a population thereof generated by an evolutionary process, whereinthe selected template is optimal among the population thereof based on apredetermined fitness measure; and the predetermined fitness measure isa correlation coefficient calculated between the image block of thebinary image and an image block of a restored binary image.
 9. Themethod according to claim 8 further comprising: determining the firstimage context and the second image context from spatial frequencycontent of the binary image.
 10. A method to convert a binary imageobtained from an image having a first resolution to a multi-bit pixelimage having a second resolution, the method comprising: forming ahybrid lookup table having a plurality of storage locations respectivelyaddressable by a plurality of bit patterns, at least two of the storagelocations being addressed by a like one of the bit patterns; storing ineach of the storage locations of the hybrid lookup table a multi-bitpixel value obtained from a multi-bit training image at a pixel locationcorresponding to a binary pixel in an image block of a binary trainingimage, the multi-bit training image having the second resolution, thebit pattern to address each of the storage locations being formed fromstates of a plurality of binary pixels in the image block of the binarytraining image, the at least two of the storage locations storing afirst multi-bit pixel value and a second multi-bit pixel value;assigning to a pixel in the multi-bit pixel image the first multi-bitpixel value when the image block in the binary image having pixel statescorresponding to the like bit pattern is in a first image context andassigning the second multi-bit pixel value when the image block in thebinary image having the pixel states corresponding to the like bitpattern is in a second image context; and determining an optimumtemplate of locations in the image block from which to obtain the statesof the plurality of binary pixels in the image block, the determining ofthe optimum template including selecting the template from a populationthereof generated by an evolutionary process, wherein the selectedtemplate is optimal among the population thereof based on apredetermined fitness measure; and the predetermined fitness measure isa correlation coefficient calculated between the image block of thebinary training image and an image block of a restored binary image. 11.The method according to claim 10 further comprising: forming themulti-bit training image from a second multi-bit training image havingthe first resolution.
 12. The method according to claim 11, wherebyforming the multi-bit training image includes generating a pixel valuein the first training image from a pixel in the first image context ofthe second training image and generating another pixel value in thefirst training image from a pixel in the second image context of thesecond training image.
 13. The method according to claim 11, wherebyforming the multi-bit training image includes interpolating the secondmulti-bit training image.
 14. The method according to claim 10 furthercomprising: determining the first image context and the second imagecontext from spatial frequency content of the binary image.
 15. Anapparatus to convert a binary image into a multi-bit pixel image,comprising: a storing part to store a first multi-bit pixel value and asecond multi-bit pixel value each addressed by a like bit pattern formedfrom states of binary image pixels; a converting part to assign to apixel in the multi-bit pixel image one of the first multi-bit pixelvalue and the second multi-bit pixel value addressed by the like bitpattern corresponding to the states of the binary image pixels in animage block of the binary image containing a binary pixel correspondingto the pixel in the multi-bit pixel image, the converting part to assignthe first multi-bit pixel value to the pixel when the image block in thebinary image is in a first image context and to assign the secondmulti-bit pixel value to the pixel when the image block in the binaryimage is in a second image context; and determining an optimum templateof locations in the image block from which to obtain the states of thebinary image pixels, the determining of the optimum template includingselecting the template from a population thereof generated by anevolutionary process, wherein the selected template is optimal among thepopulation thereof based on a predetermined fitness measure; and thepredetermined fitness measure is a correlation coefficient calculatedbetween the image block of the binary image and an image block of arestored binary image.
 16. The apparatus according to claim 15, furthercomprising an input part to receive the binary image at a resolutionless than the multi-bit pixel image.